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1. A Mechanism Design Approach to Vendor Managed Inventory

   https://doi.org/10.1287/mnsc.2019.3297  

   Kadiyala, Bharadwaj; Özer, Özalp; Bensoussan, Alain (October 2019, Management Science)

   This paper studies an inventory management problem faced by an upstream supplier that is in a collaborative agreement, such as vendor-managed inventory (VMI), with a retailer. A VMI partnership provides the supplier an opportunity to manage in- ventory for the supply chain in exchange for point-of-sales (POS)- and inventory-level information from the retailer. However, retailers typically possess superior local market information and as has been the case in recent years, are able to capture and analyze customer purchasing behavior beyond the traditional POS data. Such analyses provide the retailer access to market signals that are otherwise hard to capture using POS information. We show and quantify the implication of the financial obligations of each party in VMI that renders communication of such important market signals as noncredible. To help insti- tute a sound VMI collaboration, we propose learn and screen—a dynamic inventory mechanism—for the supplier to effectively manage inventory and information in the supply chain. The proposed mechanism combines the ability of the supplier to learn about market conditions from POS data (over multiple selling periods) and dynamically de- termine when to screen the retailer and acquire his private demand information. Inventory decisions in the proposed mechanism serve a strategic purpose in addition to their classic role of satisfying customer demand. We show that our proposed dynamic mechanism significantly improves the supplier’s expected profit and increases the efficiency of the overall supply chain operations under a VMI agreement. In addition, we determine the market conditions in which a strategic approach to VMI results in significant profit im- provements for both firms, particularly when the retailer has high market power (i.e., when the supplier highly depends on the retailer) and when the supplier has relatively less knowledge about the end customer/market compared with the retailer. 

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2. Privacy-Preserving Dynamic Personalized Pricing with Demand Learning

   https://doi.org/10.1287/mnsc.2021.4129  

   Chen, Xi; Simchi-Levi, David; Wang, Yining (October 2021, Management Science)

   null (Ed.)

   The prevalence of e-commerce has made customers’ detailed personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When using personalized information, the question of how to protect the privacy of such information becomes a critical issue in practice. In this paper, we consider a dynamic pricing problem over T time periods with an unknown demand function of posted price and personalized information. At each time t, the retailer observes an arriving customer’s personal information and offers a price. The customer then makes the purchase decision, which will be utilized by the retailer to learn the underlying demand function. There is potentially a serious privacy concern during this process: a third-party agent might infer the personalized information and purchase decisions from price changes in the pricing system. Using the fundamental framework of differential privacy from computer science, we develop a privacy-preserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customer’s information and purchasing decisions. To this end, we first introduce a notion of anticipating [Formula: see text]-differential privacy that is tailored to the dynamic pricing problem. Our policy achieves both the privacy guarantee and the performance guarantee in terms of regret. Roughly speaking, for d-dimensional personalized information, our algorithm achieves the expected regret at the order of [Formula: see text] when the customers’ information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to [Formula: see text]. This paper was accepted by J. George Shanthikumar, big data analytics. 

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3. Does blame shifting work?

   https://doi.org/10.1145/3371133  

   Lazarek, Lukas; King, Alexis; Sundar, Samanvitha; Findler, Robert_Bruce; Dimoulas, Christos (December 2019, Proceedings of the ACM on Programming Languages)

   Contract systems, especially of the higher-order flavor, go hand in hand with blame. The pragmatic purpose of blame is to narrow down the code that a programmer needs to examine to locate the bug when the contract system discovers a contract violation. Or so the literature on higher-order contracts claims. In reality, however, there is neither empirical nor theoretical evidence that connects blame with the location of bugs. The reputation of blame as a tool for weeding out bugs rests on anecdotes about how programmers use contracts to shift blame and their attention from one part of a program to another until they discover the source of the problem. This paper aims to fill the apparent gap and shed light to the relation between blame and bugs. To that end, we introduce an empirical methodology for investigating whether, for a given contract system, it is possible to translate blame information to the location of bugs in a systematic manner. Our methodology is inspired by how programmers attempt to increase the precision of the contracts of a blamed component in order to shift blame to another component, which becomes the next candidate for containing the bug. In particular, we construct a framework that enables us to ask for a contract system whether (i) the process of blame shifting causes blame to eventually settle to the component that contains the bug; and (ii) every shift moves blame ``closer'' to the faulty component. Our methodology offers a rigorous means for evaluating the pragmatics of contract systems, and we employ it to analyze Racket's contract system. Along the way, we uncover subtle points about the pragmatic meaning of contracts and blame in Racket: (i) the expressiveness of Racket's off-the-shelf contract language is not sufficient to narrow down the blamed portion of the code to the faulty component in all cases; and (ii) contracts that trigger state changes (even unexpectedly, perhaps in the runtime system's data structures or caches) interfere with program evaluation in subtle ways and thus blame shifting can lead programmers on a detour when searching for a bug. These points highlight how evaluations such as ours suggest fixes to language design. 

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4. Formalizing and Reasoning About Supply Chain Contracts Between Agents

   Dylan Flynn; Chasity Nadeau; Jeannine Shantz; Marcello Balduccini; Tran Cao Son; Edward R. Griffor (January 2023, Proceedings of the 25th International Symposium Practical Aspects on Declarative Languages -)

   Inspired by the recent problems in supply chains, we propose an approach to declarative modeling of contracts between agents that will eventually support reasoning about resilience of and about ways to improve supply chains. Specifically, we present a high-level language for specifying and reasoning about contracts over action domains of agents. We assume that the behavior of the agents can be formally expressed through action theories and view a contract as a collection of constraints. Each constraint specifies the responsibility of an agent to achieve a certain result by a deadline. Each agent also has a mapping between constraints and the agent’s concerns, i.e. issues that the agent is concerned about, which are modeled in accordance with the CPS Framework proposed by the National Institute of Standards and Technology. We discuss how common questions related to the fulfillment of a contract or the concerns of the agents can be answered and computed via Answer Set Programming. 

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5. Decentralized Control of Constrained Linear Systems via Assume-Guarantee Contracts

   https://doi.org/10.23919/ACC45564.2020.9147215  

   Lin, Weixuan; Bitar, Eilyan (July 2020, 2020 American Control Conference (ACC))

   null (Ed.)

   We consider the decentralized control of a discretetime, linear system subject to exogenous disturbances and polyhedral constraints on the state and input trajectories. The underlying system is composed of a finite collection of dynamically coupled subsystems, where each subsystem is assumed to have a dedicated local controller. The decentralization of information is expressed according to sparsity constraints on the state measurements that each local controller has access to. In this context, we investigate the design of decentralized controllers that are affinely parameterized in their measurement history. For problems with partially nested information structures, the optimization over such policy spaces is known to be convex. Convexity is not, however, guaranteed under more general (nonclassical) information structures in which the information available to one local controller can be affected by control actions that it cannot access or reconstruct. With the aim of alleviating the nonconvexity that arises in such problems, we propose an approach to decentralized control design where the information-coupling states are effectively treated as disturbances whose trajectories are constrained to take values in ellipsoidal contract sets whose location, scale, and orientation are jointly optimized with the underlying affine decentralized control policy. We establish a natural structural condition on the space of allowable contracts that facilitates the joint optimization over the control policy and the contract set via semidefinite programming. 

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